Modern data centers often comprise thousands of hosts that operate collectively to service requests from even larger numbers of remote clients. During operation, components of these data centers can produce significant volumes of machine-generated data. The unstructured nature of much of this data has made it challenging to perform indexing and searching operations because of the difficulty of applying semantic meaning to unstructured data. As the number of hosts and clients associated with a data center continues to grow, processing large volumes of machine-generated data in an intelligent manner and effectively presenting the results of such processing continues to be priority.
In particular, where multiple users from a single institution or even multiple institutions access the same data sets, maintaining a consistent user experience across all instances and interfaces in real time presents a distinct challenge, particularly when user customizations and configurations are supplied nearly simultaneously. Conventional techniques typically distribute a centralized configuration data set among all interfaces. However, as the size of the data set scales in relation to the data being processed, distributing entire data sets can become inefficient and result in undesirable delays, potential conflicts, and a compromised user experience.